Decoding Sequence Classification Models for Acquiring New Biological Insights

Bodenhofer, Ulrich; Kothmeier, Andreas; Abfalter, Ingrid G.; Mahrenholz, Carsten C.; Hochreiter, Sepp

doi:10.1038/npre.2010.4708

Cited by 1 publication

(1 citation statement)

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“…Canadian scholars Hassanabadi et al [9] present a novel, mobility-based clustering scheme for Vehicular Ad hoc Networks, which forms clusters using the Affinity Propagation algorithm in a distributed manner. Austrian scholars Bodenhofer U. et al [10] provided an R implementation of AP algorithm to account for the ubiquity of R in bioinformatics. By introducing an idea of the emoticon to AP, Zhang Lumin et al [11] proposed a novel approach to mine online events based on emoticons.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised affinity propagation based on density peaks

et al. 2016

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Original scientific paper In view of the unsatisfying clustering effect of affinity propagation (AP) clustering algorithm when dealing with data sets of complex structures, a semi-supervised affinity propagation clustering algorithm based on density peaks (SAP-DP) was proposed in this paper. The algorithm uses a new algorithm of density peaks (DP) which has the advantage of the manifold clustering with the idea of semi-supervised, builds pairwise constraints to adjust the similarity matrix, and then executes the AP clustering. The results of the simulation experiments validated that the proposed algorithm has better clustering performance compared with conventional AP.Keywords: Affinity Propagation; Density Peaks; pairwise constraints; semi-supervised learning Polu-nadzirana propagacija afiniteta temeljena na vršnoj gustoćiIzvorni znanstveni članak Zbog nezadovoljavajućeg učinka grupiranja (klasteriranja) pomoću algoritma grupiranja propagacijom afiniteta (AP -affinity propagation) u slučaju nizova podataka složene strukture, u radu se predlaže polu nadzirani algoritam grupiranja propagacije afiniteta temeljen na vršnoj gustoći (SAP-DP). Taj algoritam primjenjuje novi algoritam vršne gustoće (DP -density peaks) čija je prednost višestruko grupiranje uz polu-nadziranje, izgradnja udvojenih ograničenja zbog usklađivanja s matricom sličnosti, a zatim izvršenje grupiranja propagacijom afiniteta. Rezultati simulacijskih eksperimenata potvrdili su da je grupiranje predloženim algoritmom učinkovitije od grupiranja konvencionalnom propagacijom afiniteta (AP).

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Section: Introductionmentioning

confidence: 99%